2017 考博英语 阅读题源经济学人文章每日精析（四十九）

考博英语阅读大部分博士研究生招生院校都使用《经济学人》杂志文章作为题源，考博信息网|http://www.kaoboinfo.com教育考博频道为考博生们将其中的文章进行深度分析，希望能提高大家的考博英语阅读水平，读懂长难句。

导读

X射线是由于原子中的电子在能量相差悬殊的两个能级之间的跃迁而产生的粒子流，是波长介于紫外线和γ射线 之间的电磁辐射。其波长很短约介于0.01~100埃之间。由德国物理学家W.K.伦琴于1895年发现，故又称伦琴射线。

伦琴射线具有很高的穿透本领，能透过许多对可见光不透明的物质，如墨纸、木料等。这种肉眼看不见的射线可以使很多固体材料发生可见的荧光，使照相底片感光以及空气电离等效应。波长小于0.1埃的称超硬X射线，在0.1~1埃范围内的称硬X射线，1~100埃范围内的称软X射线。

Machines are learning to find concealed weapons in X-ray scans

机器正在尝试用X射线扫描隐藏的武器

Artificial intelligence moves into security scanning

人工智能正在逐渐应用于安全扫描中

Dec 3rd 2016

2016年12月3日

Prettier than an x-ray

比X射线更好

EVERY day more than 8,000 containers flow through the Port of Rotterdam. But only a fraction are selected to pass through a giant x-ray machine to check for illicit contents. The machine, made by Rapiscan, an American firm, can capture images as the containers move along a track at 15kph (9.3mph). But it takes time for a human to inspect each scan for anything suspicious—and in particular for small metallic objects that might be weapons. (Imagine searching an image of a room three metres by 14 metres crammed to the ceiling with goods.) To increase this inspection rate would require a small army of people.

每天有8000多个集装箱像流水一样通过鹿特丹港，但是只有一小部分会被挑出来，用大型X射线扫描机器进行非法物品检查。这款由美国Rapiscan公司制造的机器能够捕捉到以15千米每小时(每小时9.3英里)的速度沿轨道移动的集装箱图片。但是要在每张扫描图片里找可疑物品是一件很耗人时间的事(特别是那些可能是武器的小型金属体)。(想象一下，搜查一个三乘十四米物品堆到天花板的屋子的画面)想要提高搜查效率的话，就要一小队人去搜查。

A group of computer scientists at University College London (UCL), led by Lewis Griffin, may soon speed up the process by employing artificial intelligence. Dr Griffin is being sponsored by Rapiscan to create software that uses machine-learning techniques to scan the x-ray images. Thomas Rogers, a member of the UCL team, estimates that it takes a human operator about ten minutes to examine each X-ray. The UCL system can do it in 3.5 seconds.

伦敦大学学院一个由路易斯·格里芬带领计算机科学家团队，可能很快就能通过人工智能来加快这一进程。Rapiscan公司赞助格里芬博士去研发软件。装了此软件的机器可以自学X光图片扫描技术。据团队成员托马斯·罗杰斯估计检查一个X光图片需要花费操作员十分钟的时间，但是UCL系统只需3.5秒就完成了。

Dr Griffin’s team trained its system on hundreds of thousands of container scans provided by Rapiscan. The scans were missing concealed metallic objects that might pose a threat, so the UCL team took a separate database of x-rayed weapons and hid them in the container images. A paper the group presented at the Imaging for Crime Detection and Prevention conference in Madrid last week showed that in tests, the system spotted nine out of ten hidden metallic objects. Only six in every hundred readings flagged a weapon when there was nothing. (读者试译)Dr Griffin says this false positive rate has been reduced to one in every 200 since the paper was written in August. The group’s software has also been trained to detect concealed cars.

Rapiscan公司提供给格里芬博士成百上千的集装箱扫描仪，供他们测试这个系统。扫描仪无法扫描到的隐藏金属物体可能会构成威胁,所以伦敦大学学院的这个团队给用X光扫描过的武器建了一个数据库并把它们藏在集装箱扫描图像里。格里芬博士说自从8月份写这篇论文以来，这个误检率已经降到了二百分之一。该集团的软件也可训练以便检测隐藏的汽车。

The UCL team hopes to test its software shortly on real containers, some with small weapons deliberately hidden inside. Assuming that works, Dr Griffin plans to integrate the artificial-intelligence system into Rapiscan’s scanning systems over the next few months. The team is also aiming to train the system to detect “anomalies”—the machine-learning equivalent of a human hunch that something is not quite right about a scan. That could, for instance, be something unusual in the way things are positioned inside the container. Given enough data, the scientists reckon computers can train themselves to identify discrepancies like this.

伦敦大学学院的团队希望尽快用真实的、里面有人故意隐藏小型武器的集装箱来检测此软件。假如这个软件有用，格里芬博士计划在接下的几个月里把人工智能系统整合到Rapiscan公司的扫描系统中。团队准备特意训练此系统来检测“异常”现象(机器学习和人类直觉扫描有误是一样的)这能是由于物体放入容器的方式很特别。但是掌握了充足数据的科学家们认为，电脑可以通过自我训练自来识别这类差异。

It is not just in ports where machine learning could speed up scanning. Weary travellers dragging themselves through the slow crawl of airport security could also benefit. Suitcases are smaller than containers, and their contents are more predictable, so humans are able to inspect their X-rays quickly and thoroughly (although regular rest breaks are still needed).

机器自学提高扫描速度这一项技术，不单单只能用在港口上。拖着疲惫的身躯缓慢通过机场安检的旅客也能从中受益。手提箱比集装箱要小，里面的物品也更容易检测到。所以用X射线能够更快，更彻底地扫描乘客(尽管仍然需要有时间间隔)。

Toby Breckon of Durham University is working on automated x-ray analysis to detect small items of the sort that might be contained in passengers’ cabin and hold bags. He says his group has already had an algorithm installed in commercial scanning systems. Dr Breckon thinks intelligent scanning systems will at first operate in the background at airports, for instance rechecking bags in case human inspectors have missed something. They might also be used to flag bags that could be worth a manual inspection.

杜伦大学的托比Breckon正致力于自动化X射线分析，以检测客舱，乘客手拿包里的小物品。他说他的团队已经创造出了可用于商业扫描系统的算法。Breckon博士认为智能扫描系统将会最先用在机场，例如复查行李包裹，以防检查员漏检。也可能用来标记那些需要人工检查的行李包裹上。

In time, however, automated screening systems may go from being useful tools for human operators to outperforming them. If his team can get its hands on the large amounts of security imagery it needs to feed into its software, Dr Griffin thinks container scanning, at least, might be entirely automated. Perhaps bag-scanning at airports might go the same way. But there will still be a need for people. Someone has to be around to check inside containers and bags with suspicious contents.

然而，早晚有一天，自动筛选系统可能从对操作员有用的工具变得比他们表现得更为出色的工具。格里芬博士认为，如果他的团队能够得到大量安全图像供软件学习的话，至少集装箱扫描可以完全自动化。也许机场行李包裹扫描包也会全自动化。但是检查仍需要人类，需要有人检查集装箱内或行李包裹里面的可疑物品。